For decades, the power in finance was straightforward: those with the capital held the advantage. But our guest today, SaaS and software expert Vijay Raina, argues that this foundation is cracking. He posits that the true advantage has shifted from the institutions that hold money to the platforms that hold real-time, operational truth. This isn’t just about a slicker user interface; it’s a fundamental rewiring of how business risk is understood and how capital is allocated.
In our conversation, we’ll explore how vertical SaaS platforms are using their deep operational insights to build a more accurate picture of a business’s health than any traditional bank ever could. We’ll break down the concept of “informational compounding” and how it creates an almost unbeatable defensive moat. Finally, we’ll discuss the strategic imperative for SaaS companies to effectively become the banks for their industries—without ever needing a bank charter—and what this means for the future of business lending.
You argue that banks’ informational advantage is broken. Citing examples like Toast or Shopify, what specific real-time data gives SaaS a truer picture of a business’s health than traditional financial statements, and can you walk us through an example of this in action?
Absolutely. The core of the issue is the difference between looking at a photograph versus watching a live video feed. A bank looks at quarterly financial statements, credit histories, and collateral—that’s the photograph. It’s a static, historical snapshot. A vertical SaaS platform, on the other hand, is the live feed. It sees the business as it breathes. For a restaurant using a system like Toast, we’re not just seeing monthly revenue. We’re seeing minute-by-minute cover counts, the margin on every single menu item sold, and the drop-off in reservations for the Friday night rush before it even hits the cash flow. Imagine a restaurant owner sees their reservation-to-seated conversion rate decline for three straight weeks. That’s a powerful leading indicator of trouble. The SaaS platform sees that pattern emerge in real-time. The bank won’t have a clue until the end of the quarter when the P&L statement looks weak, but by then, the problem has already taken root. This is the structural inversion: SaaS now holds the real-time truth, and that truth is proving to be more valuable than the capital itself.
Shopify Capital has deployed over five billion dollars using its own data. Can you break down the step-by-step process of how this internal telemetry leads to more accurate risk pricing than a bank’s model, and what are the most critical metrics you look for?
Shopify Capital is the perfect case study because it illustrates a complete paradigm shift in underwriting. A traditional bank would ask a Shopify merchant for years of financial statements and tax returns. Shopify doesn’t need to. It has a high-granularity record of the business’s entire commercial life. The process starts with observing the operational telemetry. We’re not just looking at total sales. We’re looking at SKU-level acceleration—is a new product suddenly taking off? We’re watching conversion behavior—are more browsers becoming buyers? We’re tracking customer cohorts and repeat purchase rates. This data allows Shopify to see the causes of financial performance, not just the symptoms. So, when they offer capital, it’s not based on last year’s profits; it’s based on the observable, upward momentum of the business today. The risk is priced more accurately because the model understands the underlying physics of that specific e-commerce operation. This is why they’ve been able to deploy over five billion dollars successfully. They’re not just a lender; they’re the operating system that understands the merchant better than anyone.
You mention “informational compounding” as the real moat. Beyond increasing ARPU, how does embedding finance create structural defensibility for a SaaS platform? Can you share a metric or anecdote that shows how this changes the customer relationship from merely transactional to deeply structural?
“Informational compounding” is the secret weapon here, and it’s far more powerful than simple customer stickiness. When a SaaS platform embeds finance, it fundamentally changes the customer’s switching costs. It’s no longer about the hassle of migrating data to a new software vendor. It’s about ripping out the financial infrastructure that keeps your business alive. Think about it: if your daily operations, your payment processing, and your access to working capital all flow through one system, leaving that system becomes an existential business risk. The relationship ceases to be transactional—a monthly software fee—and becomes deeply structural. A powerful indicator of this is what we see at Toast. They report that customers who use their more deeply integrated products, including financial services, generate over 30% higher ARPU and have lower churn. That’s not just a software feature; that’s evidence of a structural bond. Every loan that’s offered, every repayment that’s made, it all becomes new data feeding back into the platform’s risk model. The model gets smarter with every single transaction, creating an intelligence gap that no external bank, relying on old data, can ever hope to cross. That’s the moat.
The piece advises SaaS to “become banks without ever becoming banks.” For a vertical SaaS platform looking to start this journey, what are the critical first steps to embedding finance, and how should they decide between partners like BaaS providers versus sponsor banks?
The most critical first step isn’t technical; it’s a mindset shift. The platform must recognize that they are sitting on the most valuable asset in modern finance: the data. They must own what I call the “intellectual core” of finance for their vertical. This means deeply understanding their customers’ behavior, their risk patterns, and the precise timing of their liquidity needs. They should never outsource this intelligence. The practical side—the regulatory compliance, the capital requirements, the licensed infrastructure—can and should be abstracted away. That’s where partners come in. The choice between a Banking-as-a-Service (BaaS) provider and a sponsor bank really depends on the SaaS platform’s goals. A BaaS provider often offers a more integrated, tech-forward solution that’s faster to market, which is great for a platform just starting out. A direct relationship with a sponsor bank might offer more control and better economics at scale, but it requires more heavy lifting from the SaaS company. The key is to remember the division of labor: the SaaS platform provides the intelligence, the partner provides the regulated rails.
What is your forecast for embedded finance? Given projections of it hitting $571 billion by 2033, which industries do you see being most transformed by this shift in the next five years, and what will be the biggest challenge for traditional banks as this accelerates?
My forecast is that the growth will be even more transformative than the numbers suggest. That projection of the market reaching nearly $571 billion by 2033, growing at over 21% annually, feels entirely achievable because this isn’t a fad; it’s a fundamental realignment. In the next five years, the industries that will be most visibly transformed are the ones where vertical SaaS is already the lifeblood: restaurants, retail and e-commerce, and complex field services like HVAC and plumbing. Think Toast, Shopify, and ServiceTitan. They are already so deeply embedded in the operational workflow that adding finance is a natural, almost inevitable, extension. The biggest challenge for traditional banks is that their entire value proposition is being hollowed out from the inside. They are at risk of being relegated to the role of a utility, providing the regulated, capital-intensive plumbing while the SaaS platforms control the intelligence layer where the real value and profits are made. Their challenge isn’t to build a better app; it’s to find a new source of informational advantage in a world where they no longer have it. And frankly, I don’t see a clear path for them to win that back. The data has left the building.
